Meta-SR: A Magnification-Arbitrary Network for Super-Resolution Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun [ pdf ]
Blind Super-Resolution With Iterative Kernel Correction Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong [ pdf ]
Camera Lens Super-Resolution Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu [ pdf ], [ supp ]
Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]
Towards Real Scene Super-Resolution With Raw Images Xiangyu Xu, Yongrui Ma, Wenxiu Sun [ pdf ]
ODE-Inspired Network Design for Single Image Super-Resolution Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, Jian Cheng [ pdf ], [ supp ]
Feedback Network for Image Super-Resolution Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu [ pdf ], [ supp ]
Recurrent Back-Projection Network for Video Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ], [ supp ]
Image Super-Resolution by Neural Texture Transfer Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi [ pdf ]
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho [ pdf ], [ supp ]
3D Appearance Super-Resolution With Deep Learning Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc Van Gool [ pdf ], [ supp ]
Fast Spatio-Temporal Residual Network for Video Super-Resolution Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, Dacheng Tao [ pdf ], [ supp ]
Residual Networks for Light Field Image Super-Resolution Shuo Zhang, Youfang Lin, Hao Sheng [ pdf ]
Second-Order Attention Network for Single Image Super-Resolution Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, Lei Zhang [ pdf ], [ pdf ]
Hyperspectral Image Super-Resolution With Optimized RGB Guidance Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang [ pdf ]
Learning Parallax Attention for Stereo Image Super-Resolution Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo [ pdf ], [ supp ]
Face Super-resolution Guided by Facial Component Heatmaps Xin Yu, Basura Fernando, Bernard Ghanem, Fatih Porikli, Richard Hartley [ pdf ]
Image Super-Resolution Using Very Deep Residual Channel Attention Networks Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu [ pdf ]
Super-Resolution and Sparse View CT Reconstruction Guangming Zang, Mohamed Aly, Ramzi Idoughi, Peter Wonka, Wolfgang Heidrich [ pdf ]
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn [ pdf ]
SRFeat: Single Image Super-Resolution with Feature Discrimination Seong-Jin Park, Hyeongseok Son, Sunghyun Cho, Ki-Sang Hong, Seungyong Lee [ pdf ]
To learn image super-resolution, use a GAN to learn how to do image degradation first Adrian Bulat, Jing Yang, Georgios Tzimiropoulos [ pdf ]
Multi-scale Residual Network for Image Super-Resolution Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang [ pdf ]
Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs Adrian Bulat, Georgios Tzimiropoulos [ pdf ] [ Supp ]
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers [ pdf ] [ Supp ]
Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy [ pdf ]
Fast and Accurate Single Image Super-Resolution via Information Distillation Network Zheng Hui, Xiumei Wang, Xinbo Gao [ pdf ]
Image Super-Resolution via Dual-State Recurrent Networks Wei Han, Shiyu Chang, Ding Liu, Mo Yu, Michael Witbrock, Thomas S. Huang [ pdf ]
Deep Back-Projection Networks for Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ] [ Supp ]
Residual Dense Network for Image Super-Resolution Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu [ pdf ]
FSRNet: End-to-End Learning Face Super-Resolution With Facial Priors Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, Jian Yang [ pdf ]
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution Ying Qu, Hairong Qi, Chiman Kwan [ pdf ]
“Zero-Shot” Super-Resolution Using Deep Internal Learning Assaf Shocher, Nadav Cohen, Michal Irani [ pdf ]
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, Seon Joo Kim [ pdf ] [ Supp ]
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]
Feature Super-Resolution: Make Machine See More Clearly Weimin Tan, Bo Yan, Bahetiyaer Bare [ pdf ]
Frame-Recurrent Video Super-Resolution Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown [ pdf ]
Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light Yuki Shiba, Satoshi Ono, Ryo Furukawa, Shinsaku Hiura, Hiroshi Kawasaki [ pdf ] [ Supp ] [ video ]
Robust Video Super-Resolution With Learned Temporal Dynamics Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang [ pdf ]
Detail-Revealing Deep Video Super-Resolution Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia [ pdf ] [ video ]
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis Mehdi S. M. Sajjadi, Bernhard Scholkopf, Michael Hirsch [ pdf ] [ Supp ][ video ]
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence Haesol Park, Kyoung Mu Lee [ pdf ] [ Supp ]
Image Super-Resolution Using Dense Skip Connections Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao [ pdf ]
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang [ pdf ]
Image Super-Resolution via Deep Recursive Residual Network Ying Tai, Jian Yang, Xiaoming Liu [ pdf ]
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ] [ video ]
Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ]
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization Renwei Dian, Leyuan Fang, Shutao Li [ pdf ] [ poster ]
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding Yawen Huang, Ling Shao, Alejandro F. Frangi [ pdf ] [ poster ]
Reference Guided Deep Super-Resolution via Manifold Localized External Compensation Wenhan Yang, Sifeng Xia, Jiaying Liu, and Zongming Guo Accepted by IEEE Trans. on Circuit System for Video Technology (TCSVT), June 2018. [ project ]
Joint-Feature Guided Depth Map Super-Resolution With Face Priors Shuai Yang, Jiaying Liu, Yuming Fang, and Zongming Guo IEEE Trans. on Cybernetics (TCYB), Vol.48, No.1, pp.399-411, Jan. 2018. [ project ]
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo and Shuicheng Yan IEEE Trans. on Image Processing (TIP), Vol.26, No.12, pp.5895-5907, Dec. 2017. [ project ]
Retrieval Compensated Group Structured Sparsity for Image Super-Resolution Jiaying Liu, Wenhan Yang, Xinfeng Zhang, and Zongming Guo IEEE Trans. on Multimedia (TMM), Vol.19, No.2, pp.302-216, Feb. 2017. [ project ]
ECCV2016 Accelerating the Super-Resolution Convolutional Neural Network Chao Dong, Chen Change Loy, Xiaoou Tang [ project ]
Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Li Fei-Fei [ pdf ]
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks Yifan Wang, Lijun Wang, Hongyu Wang, Peihua Li [ pdf ]
Deeply-Recursive Convolutional Network for Image Super-Resolution Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]
Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang [ pdf ]
Image Super-Resolution Using Deep Convolutional Networks Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) SRCNN [ project ] [ pdf ] [ supplementary material ]
Neighborhood Regression for Edge-Preserving Image Super-Resolution Yanghao Li, Jiaying Liu, Wenhan Yang and Zongming Guo IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr. 2015. [ project ]
Learning a Deep Convolutional Network for Image Super-Resolution Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang ECCV 2014 SRCNN [ project ] [ pdf ]
Super-Resolution From a Single Image Daniel Glasner, Shai Bagon, Michal Irani ICCV 2009 [ project ] [ pdf ]
Video Super-Resolution Based on Spatial-Temporal Recurrent Neural Networks Wenhan Yang, Jiashi Feng, Guosen Xie, Jiaying Liu, Zongming Guo and Shuicheng Yan Computer Vision and Image Understanding (CVIU), Vol.168, pp.79-92, March. 2018. [ project ]
Video Super-Resolution With Convolutional Neural Networks Armin Kappeler ; Seunghwan Yoo ; Qiqin Dai ; Aggelos K. Katsaggelos IEEE Transactions on Computational Imaging [ pdf ]
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network Wenjia Xu; Guangluan XU; Yang Wang; Xian Sun; Daoyu Lin; Yirong WU IGARSS 2018 - 2018 IEEE International Geoscience and Remote SensingSymposium [ pdf ]
中国地质大学朱祺琪老师怎么样介绍如下：
朱祺琪，博士，中国地质大学(武汉)地理与信息工程学院副教授，硕士生导师,“地大学者”青年拔尖人才。
2013年免试攻读武汉大学硕士学位，2015年硕博连读提前攻博，师从李德仁院士、钟燕飞教授与张良培教授，2018年6月毕业于武汉大学测绘遥感信息工程国家重点实验室，获摄影测量与遥感专业工学博士学位。2018年7月以“地大学者”青年优秀人才引进至中国地质大学（武汉）地理与信息工程学院。
致力于遥感大数据智能提取分析及应用方向的研究，在RSE、IEEE TCYB、ISPRS P&RS、IEEE TGRS等国际地学、遥感和信息处理领域权威期刊上发表一作/通讯论文三十余篇，5篇SCI论文入选ESI全球1%高被引论文；获2022年国家地理信息科技进步奖一等奖，2022年全国大学生测绘学科创新创业智能大赛一等奖指导教师，第十三届青年教师教学竞赛特等奖。
第三十二届研究生科技论文报告会优秀指导老师；入选中国地质大学（武汉）2022"十佳班主任"；连续四年获中国地质大学（武汉）本科毕业论文优秀指导教师奖。
已主持国家重点研发计划项目子课题、国家自然科学基金面上项目等科研项目十余项。担任SCI 期刊Geo-spatial Information Science编委，以及Remote Sensing等SCI期刊的客座编辑；担任Remote Sensing of Environment、ISPRS Journal of Photogrammetry and Remote Sensing、IEEE Transaction on Geoscience and Remote Sensing。
IEEE Transactions on Knowledge and Data Engineering、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing、IEEE Geoscience and Remote Sensing Letter等遥感、计算机领域国际权威SCI期刊的审稿人。
指导研究生获国家奖学金、信才等专项奖学金，获校级研究生科报会特等奖；带领的研究组积极为学生提供国内外学术交流，多名组内学生受邀担任国际遥感会议IGARSS的会议联合主席；带领本科生成功立项“大学生创新创业训练计划”国家级项目8项，独立撰写SCI论文。
研究生毕业后大多进入武汉大学、中山大学、中国地质大学（武汉）等国内外著名高校和腾讯、华为、百度等知名互联网公司以及测绘、城市等事业单位任职和深造。
在 IEEE TSE、TKDE、TSMCB、TCYB，Evolutionary Computation Journal, 中国科学、科学通报等期刊及ICSE, GECCO 等知名国际会议录用、发表论文60多篇。获2010年GECCO国际会议元启发式算法领域最佳论文提名。自2004年以来，先后担任Communicationsof the ACM、IEEE TSE、TEVC、TSMCB、TCYB，Knowledge and Information Systems、中国科学、计算机学报、自动化学报等国内外20余种期刊审稿人。国家自然科学基金 、教育部博士点基金评审专家及大连市科技局评审专家。担任第25届IEA/AIE国际会议PC chair 及多个国际会议PC。兼任职务：中国计算机学会高级会员、中国计算机学会软件工程专委会委员、中国计算机学会计算机应用专委会委员、Frontiers of Computer Science 青年AE。
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun [ pdf ]
Blind Super-Resolution With Iterative Kernel Correction Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong [ pdf ]
Camera Lens Super-Resolution Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu [ pdf ], [ supp ]
Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]
Towards Real Scene Super-Resolution With Raw Images Xiangyu Xu, Yongrui Ma, Wenxiu Sun [ pdf ]
ODE-Inspired Network Design for Single Image Super-Resolution Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, Jian Cheng [ pdf ], [ supp ]
Feedback Network for Image Super-Resolution Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu [ pdf ], [ supp ]
Recurrent Back-Projection Network for Video Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ], [ supp ]
Image Super-Resolution by Neural Texture Transfer Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi [ pdf ]
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho [ pdf ], [ supp ]
3D Appearance Super-Resolution With Deep Learning Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc Van Gool [ pdf ], [ supp ]
Fast Spatio-Temporal Residual Network for Video Super-Resolution Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, Dacheng Tao [ pdf ], [ supp ]
Residual Networks for Light Field Image Super-Resolution Shuo Zhang, Youfang Lin, Hao Sheng [ pdf ]
Second-Order Attention Network for Single Image Super-Resolution Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, Lei Zhang [ pdf ], [ pdf ]
Hyperspectral Image Super-Resolution With Optimized RGB Guidance Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang [ pdf ]
Learning Parallax Attention for Stereo Image Super-Resolution Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo [ pdf ], [ supp ]
Face Super-resolution Guided by Facial Component Heatmaps Xin Yu, Basura Fernando, Bernard Ghanem, Fatih Porikli, Richard Hartley [ pdf ]
Image Super-Resolution Using Very Deep Residual Channel Attention Networks Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu [ pdf ]
Super-Resolution and Sparse View CT Reconstruction Guangming Zang, Mohamed Aly, Ramzi Idoughi, Peter Wonka, Wolfgang Heidrich [ pdf ]
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn [ pdf ]
SRFeat: Single Image Super-Resolution with Feature Discrimination Seong-Jin Park, Hyeongseok Son, Sunghyun Cho, Ki-Sang Hong, Seungyong Lee [ pdf ]
To learn image super-resolution, use a GAN to learn how to do image degradation first Adrian Bulat, Jing Yang, Georgios Tzimiropoulos [ pdf ]
Multi-scale Residual Network for Image Super-Resolution Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang [ pdf ]
Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs Adrian Bulat, Georgios Tzimiropoulos [ pdf ] [ Supp ]
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers [ pdf ] [ Supp ]
Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy [ pdf ]
Fast and Accurate Single Image Super-Resolution via Information Distillation Network Zheng Hui, Xiumei Wang, Xinbo Gao [ pdf ]
Image Super-Resolution via Dual-State Recurrent Networks Wei Han, Shiyu Chang, Ding Liu, Mo Yu, Michael Witbrock, Thomas S. Huang [ pdf ]
Deep Back-Projection Networks for Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ] [ Supp ]
Residual Dense Network for Image Super-Resolution Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu [ pdf ]
FSRNet: End-to-End Learning Face Super-Resolution With Facial Priors Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, Jian Yang [ pdf ]
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution Ying Qu, Hairong Qi, Chiman Kwan [ pdf ]
“Zero-Shot” Super-Resolution Using Deep Internal Learning Assaf Shocher, Nadav Cohen, Michal Irani [ pdf ]
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, Seon Joo Kim [ pdf ] [ Supp ]
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]
Feature Super-Resolution: Make Machine See More Clearly Weimin Tan, Bo Yan, Bahetiyaer Bare [ pdf ]
Frame-Recurrent Video Super-Resolution Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown [ pdf ]
Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light Yuki Shiba, Satoshi Ono, Ryo Furukawa, Shinsaku Hiura, Hiroshi Kawasaki [ pdf ] [ Supp ] [ video ]
Robust Video Super-Resolution With Learned Temporal Dynamics Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang [ pdf ]
Detail-Revealing Deep Video Super-Resolution Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia [ pdf ] [ video ]
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis Mehdi S. M. Sajjadi, Bernhard Scholkopf, Michael Hirsch [ pdf ] [ Supp ][ video ]
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence Haesol Park, Kyoung Mu Lee [ pdf ] [ Supp ]
Image Super-Resolution Using Dense Skip Connections Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao [ pdf ]
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang [ pdf ]
Image Super-Resolution via Deep Recursive Residual Network Ying Tai, Jian Yang, Xiaoming Liu [ pdf ]
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ] [ video ]
Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ]
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization Renwei Dian, Leyuan Fang, Shutao Li [ pdf ] [ poster ]
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding Yawen Huang, Ling Shao, Alejandro F. Frangi [ pdf ] [ poster ]
Reference Guided Deep Super-Resolution via Manifold Localized External Compensation Wenhan Yang, Sifeng Xia, Jiaying Liu, and Zongming Guo Accepted by IEEE Trans. on Circuit System for Video Technology (TCSVT), June 2018. [ project ]
Joint-Feature Guided Depth Map Super-Resolution With Face Priors Shuai Yang, Jiaying Liu, Yuming Fang, and Zongming Guo IEEE Trans. on Cybernetics (TCYB), Vol.48, No.1, pp.399-411, Jan. 2018. [ project ]
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo and Shuicheng Yan IEEE Trans. on Image Processing (TIP), Vol.26, No.12, pp.5895-5907, Dec. 2017. [ project ]
Retrieval Compensated Group Structured Sparsity for Image Super-Resolution Jiaying Liu, Wenhan Yang, Xinfeng Zhang, and Zongming Guo IEEE Trans. on Multimedia (TMM), Vol.19, No.2, pp.302-216, Feb. 2017. [ project ]
ECCV2016 Accelerating the Super-Resolution Convolutional Neural Network Chao Dong, Chen Change Loy, Xiaoou Tang [ project ]
Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Li Fei-Fei [ pdf ]
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks Yifan Wang, Lijun Wang, Hongyu Wang, Peihua Li [ pdf ]
Deeply-Recursive Convolutional Network for Image Super-Resolution Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]
Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang [ pdf ]
Image Super-Resolution Using Deep Convolutional Networks Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) SRCNN [ project ] [ pdf ] [ supplementary material ]
Neighborhood Regression for Edge-Preserving Image Super-Resolution Yanghao Li, Jiaying Liu, Wenhan Yang and Zongming Guo IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr. 2015. [ project ]
Learning a Deep Convolutional Network for Image Super-Resolution Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang ECCV 2014 SRCNN [ project ] [ pdf ]
Super-Resolution From a Single Image Daniel Glasner, Shai Bagon, Michal Irani ICCV 2009 [ project ] [ pdf ]
Video Super-Resolution Based on Spatial-Temporal Recurrent Neural Networks Wenhan Yang, Jiashi Feng, Guosen Xie, Jiaying Liu, Zongming Guo and Shuicheng Yan Computer Vision and Image Understanding (CVIU), Vol.168, pp.79-92, March. 2018. [ project ]
Video Super-Resolution With Convolutional Neural Networks Armin Kappeler ; Seunghwan Yoo ; Qiqin Dai ; Aggelos K. Katsaggelos IEEE Transactions on Computational Imaging [ pdf ]
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network Wenjia Xu; Guangluan XU; Yang Wang; Xian Sun; Daoyu Lin; Yirong WU IGARSS 2018 - 2018 IEEE International Geoscience and Remote SensingSymposium [ pdf ]
发表论文通常只有两种渠道，要么自己投，要么找论文发表机构代投，不管走哪种渠道，最后都是要发表到期刊上的。
期刊，也叫杂志，在上个世纪在出版界曾经是重量级的存在，那个时候互联网还没有兴起，人们阅读文章获取资讯远远没有现在方便，杂志就成为一个很重要的传播媒介。
但现在随着社会的进步，科技的发展，纸媒已经大大没落了，很多期刊被砍掉了，剩下来的大多数不得不自谋出路，学术期刊更是如此，因为这个受众面是很窄的，基本没法盈利，所以只能靠收取版面费来维持，当然，有国家财政拨款的那种不在这个范围。
我们现在发表学术论文，出于严谨性权威性等原因的考虑，还是要发表到纸质期刊上，编辑会用电子邮箱或者内部的系统来收稿，但不会有一个网络平台有发表论文的资质，即使是知网和万方这样的网站，也只是论文数据库，并不是论文发表平台。
所以发表论文的时候，还是要先去选取目标期刊，然后再找到这本期刊的投稿邮箱，或者是找到靠谱的论文发表机构，由代理进行代投，最后都是发表到纸质期刊上的，见刊后一两个月左右被知网收录，就可以检索到了。
既然是潍坊老乡，我也来答答这个问题吧。首先要搞清楚，评职称不是期刊说了算的，杂志社只保证期刊的合法性，最后评职称还是要看当地评审部门是怎么规定的，有些比较好的刊物，如果当地不认可那发了也没用，还有就是艺术这类的期刊确实是比较少，而且也不好了。建议还是把矛头指向一些综合性的或者教育类的期刊上，这样刊发的可能性也大一些。
山东潍坊的很多中学老师都在省级G4教育类《都市家教》杂志上发表过论文，不过，你需要评高级职称就应该清楚评高职对期刊有什么要求，以便别人更好的帮你推荐，比如，级别（省级，国家级），上网（知网，维普，万方，龙源），类别（教育类G4），提交材料时间等相关信息！
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发表论文首先要选择合适的期刊，若有条件应该请国外的同行或者国内的专家进行评价，看适合发表在什么杂志．选择期刊要注意期刊是综合期刊还是专业期刊，其次要注意期刊刊载论文的数目，刊载量多的自然容易发表成功了，然后要看的就是期刊的发表周期，尽量选择审稿周期短而明确，且发表周期短的文章．这样做一方面有利于退稿后改投他刊，另一方面保证文章的创新性。在网上发表学术论文的方式主要有两种，一种是直接向杂志社投稿，这种投稿方式比较慢，需要等待杂志社审核稿件，由于投稿在作者比较多，而杂志社的编辑人员精力有限，如果不提前投稿很可能会错过评职称的最佳时间，另一种方式就是通过论文机构投稿，例如选择期刊58网，登陆网站点击客服咨询，我们的网站客服会在第一时间帮您安排稿件的出刊。学术论文的发布流程1. 撰写，写作内容当然以作者从事的研究工作与成果形式为准，这个不作讨论。2. 投稿，首先决定投那份个刊物。作者一般在撰写论文时就要做出决定。主要决策有二个方面：一是刊物级别，另一个是刊物方向。3. 投稿回应，这是编辑部的事。作为投稿人也必须清楚知道所投期刊对投稿的回应时间与方式，否则稿件投出后心里没底。4.审稿及初审费费。稿件收到后编辑要初审，主要是看是否符合它们刊物的发文方向及稿件的宏观质量。5.修改。如果发来修改意见，你的稿件有门。6.发稿周期：对了说了这么多还没说发一篇文章倒底得多长时间，其实我也不说不清。7.好发的期刊：当然并不是所有的期刊都这么仔细认真，如果你只想发文章，不关心质量，那你可以仔细研究哪些期刊属于好发类型的，对你发文会很有帮助的，的确有一些交钱就能发的刊物。
大部分论文都在期刊上发表,CN期刊。
少数的是发表到国外的期刊,或者直接是在杂志的官网上线,比如SCI。对于大多数人来说,发表CN期刊就可以了。
期刊，定期出版的刊物。如周刊、旬刊、半月刊、月刊、季刊、半年刊、年刊等。由依法设立的期刊出版单位出版刊物。期刊出版单位出版期刊，必须经新闻出版总署批准，持有国内统一连续出版物号，领取《期刊出版许可证》。
广义上分类
从广义上来讲，期刊的分类，可以分为非正式期刊和正式期刊两种。非正式期刊是指通过行政部门审核领取“内部报刊准印证”作为行业内部交流的期刊(一般只限行业内交流不公开发行)，但也是合法期刊的一种，一般正式期刊都经历过非正式期刊过程。
正式期刊是由国家新闻出版署与国家科委在商定的数额内审批，并编入“国内统一刊号”，办刊申请比较严格，要有一定的办刊实力，正式期刊有独立的办刊方针。
“国内统一刊号”是“国内统一连续出版物号”的简称，即“CN号”，它是新闻出版行政部门分配给连续出版物的代号。“国际刊号”是“国际标准连续出版物号”的简称，即“ISSN号”，我国大部分期刊都配有“ISSN号”。
此外，正像报纸一样，期刊也可以不同的角度分类。有多少个角度就有多少种分类的结果，角度太多则流于繁琐。一般从以下三个角度进行分类：
按学科分类
以《中国图书馆图书分类法.期刊分类表》为代表，将期刊分为五个基本部类：
(1)思想(2)哲学(3)社会科学(4)自然科学(5)综合性刊物。在基本部类中，又分为若干大类，如社会科学分为社会科学总论、政治、军事、经济、文化、科学、教育、体育、语言、文字、文学、艺术、历史、地理。
按内容分类
以《中国大百科全书》新闻出版卷为代表，将期刊分为四大类：
(1)一般期刊，强调知识性与趣味性，读者面广，如我国的《人民画报》、《大众电影》，美国的《时代》、《读者文摘》等;
(2)学术期刊，主要刊载学术论文、研究报告、评论等文章，以专业工作者为主要对象;
(3)行业期刊，主要报道各行各业的产品、市场行情、经营管理进展与动态，如中国的《摩托车信息》、《家具》、日本的《办公室设备与产品》等;
(4)检索期刊，如我国的《全国报刊索引》、《全国新书目》，美国的《化学文摘》等。
按学术地位分类
可分为核心期刊和非核心期刊（通常所说的普刊）两大类。
关于核心期刊
核心期刊，是指在某一学科领域(或若干领域)中最能反映该学科的学术水平，信息量大，利用率高，受到普遍重视的权威性期刊。
不要选择它是你自己的决定。
由于申请专利的技术具有新颖性，因此发明人有了技术成果之后，应首先申请专利，再发表论文，以免因发表论文过早公开技术而失去新颖性，丧失申请专利的机会。具备新颖性、创造性和实用性是取得专利权的实质条件。
之前发表的论文是不能用来结题的。科研的流程：第一步：课题拟题第二步：课题材料（申请书，可行性报告，查新委托书）编辑和查新第三步：课题查新材料送县区科技局盖章第四步：课题查新材料送市科技局盖章立项签合同第五步：准备结题材料结题这是大概的流程的。
不可以。大学中论文是对于立项内容的一个总结，不可以先写论文再立项，不符合要求，并且先写论文的情况下，论文中的观点和内容并没有证据支撑。